Environmental research is moving beyond discipline-specific approaches as environmental problems become increasingly interconnected across processes, scales, and societal systems. Drawing on discussions from the Critical Inquiry Forum on Cross-Disciplinary Integration and Innovative Frontiers at the 6th Youth Forum on Frontiers of Environmental Science and Engineering, we identify two central considerations for advancing interdisciplinary environmental research. First, meaningful interdisciplinarity depends on integrating disciplinary expertise around common environmental problems rather than simply combining methods from different fields, with collaboration organized around clearly defined problems and complementary disciplinary perspectives. Second, integrative innovation requires shared scientific questions and evaluation criteria, mechanistic understanding grounded in process analysis and risk assessment, and translation into practice through artificial intelligence, sensing technologies, engineering validation, and management tools. Collectively, these elements can connect fundamental understanding with technological development and practical environmental needs. These considerations outline a problem-driven pathway linking interdisciplinary collaboration with scientific innovation and environmental decision-making, providing a framework for addressing complex environmental challenges that cannot be resolved within individual disciplinary boundaries.
Carbon dioxide (CO2) is a key component of the global carbon cycle and major driver of climate change. Although ground-based measurements and satellite remote sensing have been conducted globally, high-resolution, high-accuracy datasets of near-surface CO2 concentrations remain scarce, limiting the understanding of their distribution and variation trends. To address this gap, we established a novel machine learning-based framework to generate global daily near-surface CO2 concentrations at a spatial resolution of 0.5° × 0.625° during 2015–2021. By integrating satellite data, ground observations, meteorological and climatic variables, a vegetation index, and anthropogenic indicators, we developed a LightGBM prediction model that effectively captures spatiotemporal CO2 patterns, achieving a correlation coefficient of 0.89 and a root-mean-square error of 3.5 ppm. The estimation results revealed that the global annual mean near-surface CO2 concentrations during 2015–2021 were 408.71 ± 2.98 ppm, with an average growth rate of 2.66 ± 0.27 ppm/yr. High growth rates were observed in Southeast Asia, the South China Sea, West Africa, and the Amazon, with the greatest increase occurring in 2016. Compared with most other regions, South Asia, a high-carbon-emission region, exhibited greater and more variable near-surface CO2 concentrations. Model interpretability analysis revealed that temperature strongly positively influenced the near-surface CO2 in tropical regions, whereas evaporation and soil type positively affected these concentrations in arid regions. Soil improvements and large-scale afforestation were potential mitigation strategies to reduce the CO2 levels in high-concentration areas. Daily high-resolution mapping enables the detection of short-term CO2 increases resulted from large-scale wildfires, supporting regional emissions verification and near-term carbon emissions assessment.
Automotive electrophoretic coating volatile organic compounds (VOCs) exhibit complex compositions that pose significant atmospheric and human health risks. These emissions are characterized by low concentrations (average 7.67 mg/m3) and a high proportion of oxygenated VOCs (OVOCs, 82.94
Environmental chemistry, a cornerstone of environmental science, faces critical challenges including research homogenization, the persistent gap between laboratory-scale discoveries and engineering-scale applications, and the need to rationally integrate emerging data-driven tools. Drawing on deliberations from the 6th Youth Forum on Frontiers of Environmental Science and Engineering, this perspective outlines three guiding principles for the discipline’s future. First, fundamental mechanistic research—particularly on interfacial reaction kinetics, radical pathways, and molecular recognition—must be strengthened to provide interpretable physicochemical bases for cross-disciplinary innovations. Second, engineering thinking should be embedded from the outset, incorporating life-cycle assessment and real-water-matrix complexities to bridge the “last mile” between high-performance materials and practical deployment. Third, artificial intelligence (AI) should be positioned as an auxiliary tool rather than a universal solution; its pattern-recognition capabilities can accelerate hypothesis generation and process optimization, but its outputs require mechanistic validation and causality scrutiny. Ultimately, the authors advocate a closed-loop paradigm of “hypothesis–prediction–validation” that integrates empirical research, AI-assisted analytics, and collaborative academia–industry–government platforms, ensuring that environmental chemistry evolves from homogenized competition toward original breakthroughs and tangible environmental benefits.
Nuclear energy plays a crucial role as a clean energy source in modern society. The use of nuclear energy will result in the generation of a large amount of radioactive nuclear wastewater. Separation of nuclides from radioactive wastewater is crucial for the safe disposal of nuclear wastes and the sustainable development of resources. However, it remains a great challenge to achieve precise separation between different radionuclide ions due to their similar properties. Herein, we constructed a radiation-resistant graphene-based membrane via ethylenediaminetetraacetic acid (EDTA) functionalization with highly stable and aligned two-dimensional subnanochannels, which exhibits adjustable ion diffusion energy barrier and ultrahigh radionuclide ion selectivity. The functional groups within the GO-EDTA channel exhibit strong affinitive binding interactions with Sr2+ and La3+. The mono/multivalent metal-ion selectivity up to 485 and 1300 for Cs+/Sr2+ and Cs+/La3+, respectively, outperforms other reported membranes. Besides, the channel can still maintain stable separation performance under irradiation conditions. Furthermore, using quartz crystal microbalance, we break down the contributions of partitioning at the pore mouth and intrapore diffusion to the overall energy barrier for salt transport, indicating that the precise separation of ions is achieved by regulating the diffusion energy barrier. This work provides a mechanism for the design of membranes with high ionion selectivity and demonstrates the application potential of nuclear resource recycling.
